These properties that a self-supervised model may discover depend on the factors of variation contained in the training data [1]}.
For instance, learning features on object-centric dataset will produce features that have object-centric properties [2]}, while training them in the wild may contain information that are related to people's general interests.
While some of these signals may be related to metadata – e.g., hashtags, GPS coordinate – or semantic information about scenes or objects, other factors may be related to human-centric properties – e.g., fairness, artistic style – that are harder to annotate automatically.
In this work, we are interested in probing which of the properties emerge in visual features trained with no supervision on as many images from across the world as possible.
